System and method for processing speech

ABSTRACT

Systems and methods for processing audio are provided. The system may include a processor to convert an audio input received via a call to text. The processor may perform a comparison between a portion of the text to one or more phrases included in a table. The processor may also make a selection of at least one of a first object or a first action based on the comparison. The processor may further route the call based on the at least one of the first object or the first action.

CLAIM OF PRIORITY

This application is a Continuation Patent Application of, and claimspriority from, U.S. patent application Ser. No. 12/750,792, filed onMar. 31, 2010, and entitled “SYSTEM AND METHOD FOR PROCESSING SPEECH,”which is a continuation of U.S. Pat. No. 7,720,203, filed on Jun. 1,2007, which is a continuation of U.S. Pat. No. 7,242,751, filed on Dec.6, 2004, each of which is hereby incorporated by reference in itsentirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to speech recognition and, moreparticularly, to speech recognition-enabled automatic call routingservice systems and methods.

BACKGROUND

Speech recognition systems are specialized computers that are configuredto process and recognize human speech and may also take action or carryout further processes. Developments in speech recognition technologiessupport “natural language” type interactions between automated systemsand users. A natural language interaction allows a person to speaknaturally. Voice recognition systems can react responsively to a spokenrequest. An application of natural language processing is speechrecognition with automatic call routing (ACR). A goal of an ACRapplication is to determine why a customer is calling a service centerand to route the customer to an appropriate agent or destination forservicing a customer request. Speech recognition technology generallyallows an ACR application to recognize natural language statements sothat the caller does not have to rely on a menu system. Natural languagesystems allow the customer to state the purpose of their call “in theirown words.”

In order for an ACR application to properly route calls, the ACR systemattempts to interpret the intent of the customer and selects a routingdestination. When a speech recognition system partially understands ormisunderstands the caller's intent, significant problems can result.Further, even in touch-tone ACR systems, the caller can depress thewrong button and have a call routed to a wrong location. When a calleris routed to an undesired system and realizes that there is a mistake,the caller often hangs up and retries the call. Another common problemoccurs when a caller gets “caught” or “trapped” in a menu that does notprovide an acceptable selection to exit the menu. Trapping a caller orrouting the caller to an undesired location leads to abandoned calls.Most call routing systems handle a huge volume of calls and, even if asmall percentage of calls are abandoned, the costs associated withabandoned calls are significant.

Current speech recognition systems, such as those sold by Speechworks™,operate utilizing a dynamic semantic model. The semantic modelrecognizes human speech and creates multiple word strings based onphonemes that the semantic model can recognize. The semantic modelassigns probabilities to each of the word strings using rules and othercriteria. However, the semantic model has extensive tables and businessrules, many that are “learned” by the speech recognition system. Thelearning portion of the system is difficult to set up and modify.Further, changing the word string tables in the semantic model can be aninefficient process. For example, when a call center moves or isassigned a different area code, the semantic system is retrained usingan iterative process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a simplified configuration of a telecommunicationsystem;

FIG. 2 is a general diagram that illustrates a method of routing calls;

FIG. 3 is a flow diagram that illustrates a method of processing androuting calls;

FIG. 4 is a table that depicts speech input and mapped synonym terms;and

FIG. 5 is a table illustrating action-object pairs and call destinationsrelating to the action object pairs.

DETAILED DESCRIPTION

In a particular embodiment, a speech recognition system includes aspeech recognition interface and a processor coupled to the speechrecognition interface. The processor converts speech received from acall at the speech recognition interface to at least one word string.The processor parses each word string of the at least one word stringinto first objects and first actions. The processor accesses a synonymtable to determine second objects and second actions based on the firstobjects and the first actions. The processor also selects a preferredobject and a preferred action from the second objects and the secondactions.

In a particular embodiment, a computerized method of processing speechincludes determining a plurality of objects based on speech input anddetermining a plurality of actions based on the speech input. Thecomputerized method includes comparing the objects and the actions withentries in a synonym table to determine synonym objects and synonymactions. The computerized method includes selecting a preferred objectand a preferred action from the synonym objects and the synonym actions.The computerized method also includes routing a call that provided thespeech input to a destination based on the preferred object and thepreferred action.

In a particular embodiment, a computerized method includes transformingspeech input from a caller into a plurality of word strings. Thecomputerized method includes converting the word strings into pairs ofobjects and actions. The computerized method includes determining from asynonym table synonym pairs from the pairs. The computerized method alsoincludes selecting a preferred pair from the synonym pairs.

Particular systems and particular methods are disclosed for processing acall by receiving caller input in a speech format and utilizing phonemesto convert the speech input into word strings. The word strings are thenconverted into at least one object and at least one action. A synonymtable is utilized to determine actions and objects. Objects generallyrepresent nouns and adjective-noun combinations while actions generallyrepresent verbs and adverb-verb combinations. The synonym table storesnatural language phrases and their relationship with actions andobjects. The actions and objects are utilized to determine a routingdestination utilizing a routing table. The call is then routed based onthe routing table. During the process, the word string, the actions, theobjects and an action-object pair can be assigned a probability value.The probability value represents a probability that the word string, theaction, or the object accurately represent the purpose or intent of thecaller.

Referring to FIG. 1, an illustrated communications system 100 thatincludes a call routing support system is shown. The communicationssystem 100 includes a speech enabled call routing system (SECRS) 118,such as an interactive voice response system having a speech recognitionmodule. The system 100 includes a plurality of potential calldestinations. Illustrative call destinations shown include servicedepartments, such as billing department 120, balance information 122,technical support 124, employee directory 126, and new customer servicedepartments 128. The communication network 116 receives calls from avariety of callers, such as the illustrated callers 110, 112, and 114.In a particular embodiment, the communication network 116 may be apublic telephone network or may be provided by a voice over Internetprotocol (VoIP) type network. The SECRS 118 may include components, suchas a processor 142, a synonym table 144, and an action-object routingmodule 140. The SECRS 118 is coupled to and may route calls to any ofthe destinations, as shown. In addition, the SECRS 118 may route callsto an agent, such as the illustrated live operator 130. An illustrativeembodiment of the SECRS 118 may be a call center having a plurality ofagent terminals attached (not shown). Thus, while only a single operator130 is shown, it should be understood that a plurality of differentagent terminals or types of terminals may be coupled to the SECRS 118,such that a variety of agents may service incoming calls. In addition,the SECRS 118 may be an automated call routing system. In a particularembodiment, the action-object routing module 140 includes anaction-object lookup table for matching action-object pairs to desiredcall routing destinations.

Referring to FIG. 2, an illustrative embodiment of an action-objectrouting module 140 is shown. In this particular embodiment, theaction-object routing module 140 includes an acoustic processing model210, semantic processing model 220, and action-object routing table 230.The acoustic model 210 receives speech input 202 and provides text 204as its output. Semantic model 220 receives text 204 from the acousticmodel 210 and produces an action-object pair 206 that is provided to theaction-object routing table 230. The routing table 230 receivesaction-object pairs 206 from semantic model 220 and produces a desiredcall routing destination 208. Based on the call routing destination 208,a call received at a call routing network 118 may be routed to a finaldestination, such as the billing department 120 or the technical supportservice destination 124 depicted in FIG. 1. In a particular embodiment,the action-object routing table 230 may be a look up table or aspreadsheet, such as Microsoft Excel™.

Referring to FIG. 3, an illustrative embodiment of a method ofprocessing a call using an automated call routing system is illustrated.The method starts at 300 and proceeds to step 302 where a speech inputsignal, such as a received utterance, is received or detected. Usingphonemes, the received speech input is converted into a plurality ofword strings or text in accordance with an acoustic model, as shown atsteps 304 and 306. In a particular embodiment, probability values areassigned to word strings based on established rules and the coherency ofthe word string. Next, at step 308, the word strings are parsed intoobjects and actions. Objects generally represent nouns andadjective-noun combinations while actions generally represent verbs andadverb-verb combinations. The actions and objects are assignedconfidence values or probability values based on how likely they are toreflect the intent of the caller. In a particular embodiment aprobability value or confidence level for the detected action and thedetected object is determined utilizing the probability value of theword string used to create the selected action and the selected object.

Many possible actions and objects may be detected or created from theword strings. The method attempts to determine and select a mostprobable action and object from a list of preferred objects and actions.To aid in this resolution a synonym table, such as the synonym table ofFIG. 4 can be utilized to convert detected actions and objects intopreferred actions and objects. Thus, detected objects and actions areconverted to preferred actions and objects and assigned a confidencelevel. The process of utilizing the synonym table can alter theconfidence level. The synonym table stores natural language phrases andtheir relationship with a set of actions and objects. Natural languagespoken by the caller can be compared to the natural language phrases inthe table. Using the synonym table, the system and method maps portionsof the natural phrases to detected objects and maps portions of thenatural spoken phrase to detected actions. Thus, the word strings areconverted into objects and actions, at steps 310 and 312 respectivelyand the selected action and object are set to the action and object thatwill be utilized to route the call. The action and object with thehighest confidence value are selected based on many criteria such asconfidence value, business rules, etc., in steps 310 and 312.

At step 310 and 312, multiple actions and objects can be detected andprovided with a probability value according to the likelihood that aparticular action or object identifies a customer's intent and thus willlead to a successful routing of the call and a dominant action anddominant object are determined. Next, at step 314, dominant objects andactions are paired together. At step 316, a paired action-object iscompared to an action-object routing table, such as the action objectrouting table of FIG. 5. The action-object routing table in FIG. 5 isgenerally a predetermined list. When objects and actions find a match,then the destination of the call can be selected at step 318, and thecall is routed, at step 320. The process ends at step 322.

Referring to FIG. 4, as an example, it is beneficial to convert wordstrings such as “I want to have” to actions such as “get.” Thissubstantially reduces the size of the routing table. When a calldestination has a phone number change, a single entry in the routingtable may accommodate the change. Prior systems may require locatingnumerous entries in a voluminous database, or retraining a sophisticatedsystem. In accordance with the present system, dozens of differentlyexpressed or “differently spoken” inputs that have the same callerintent can be converted to a single detected action-object pair.Further, improper and informal sentences as well as slang can beconnected to an action-object pair that may not bear phoneticresemblance to the words uttered by the caller. With a directly mappedlookup table such as the table in FIG. 4, speech training and learningbehaviors found in conventional call routing systems are not required.The lookup table may be updated easily, leading to a low cost of systemmaintenance.

In addition, the method may include using a set of rules to convert aword string into an object or action. In a particular example,geographic designation information, such as an area code, may be used todistinguish between two potential selections or to modify theprobability value. In the event that the lookup table of theaction-object pair does not provide a suitable response, such as whereno entry is found in the routing table, the call may be routed to ahuman operator or agent terminal in response to a failed access to theaction-object lookup table.

Traditional automatic call routing systems are able to assign a correctdestination 50-80% of the time. Particular embodiments of the disclosedsystem and method using action-object tables can assign a correctdestination 85-95% of the time. Due to higher effective call placementrates, the number of abandoned calls (i.e., caller hang-ups prior tocompleting their task) is significantly reduced, thereby reducingoperating costs and enhancing customer satisfaction. In addition, theautomated call-routing system offers a speech recognition interface thatis preferred by many customers to touch tone systems.

The disclosed system and method offers significant improvements throughdecreased reliance on the conventional iterative semantic model trainingprocess. With the disclosed system, a semantic model assigns anaction-object pair leading to increased call routing accuracy andreduced costs. In particular implementations, the correct calldestination routing rate may reach the theoretical limit of 100%,depending upon particular circumstances. In some cases, certainaction-object systems have been implemented that achieve a 100% coveragerate, hit rate, and call destination accuracy rate.

The disclosed system and method is directed generally to integration ofaction-object technology with speech enabled automated call routingtechnology. The integration of these two technologies produces abeneficial combination as illustrated. The illustrated system has beendescribed in connection with a call center environment, but it should beunderstood that the disclosed system and method is applicable to otheruser interface modalities, such as web-based interfaces, touchtoneinterfaces, and other speech recognition type systems. The disclosedsystem and method provides for enhanced customer satisfaction becausethe customer's intent can be recognized by an action-object pair and ahigh percentage of calls reach the intended destination.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments that fall within thescope of the present invention. Thus, to the maximum extent allowed bylaw, the scope of the present invention is to be determined by thebroadest permissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

What is claimed is:
 1. A system comprising: a memory; and a processorcoupled to the memory, wherein the memory stores instructions that, whenexecuted by the processor, cause the processor to perform operationscomprising: converting an audio input of a call to text; determining afirst set of objects and a second set of actions from the text;determining that a particular object of the first set is included in afirst portion of a synonym table; replacing the particular object in thefirst set with a synonym for the particular object from the synonymtable to form a modified first set; pairing an object from the modifiedfirst set with an action from the second set to form an object-actionpair; and routing the call based on the object-action pair.
 2. Thesystem of claim 1, wherein the audio input is a speech input.
 3. Thesystem of claim 1, wherein the text is associated with a word string. 4.The system of claim 1, wherein each object in the modified first set isassociated with a confidence value.
 5. The system of claim 4, whereinthe object of the object-action pair is selected to be paired with theaction based on the confidence value of the object.
 6. The system ofclaim 1, wherein the object of the object-action pair is selected to bepaired with the action based on a business rule.
 7. The system of claim1, wherein the operation of replacing the particular object in the firstset includes changing a confidence value associated with the particularobject.
 8. The system of claim 1, wherein the operations furthercomprise identifying a destination location in a routing table based onthe object-action pair.
 9. The system of claim 8, wherein the operationof routing the call includes routing the call to the destinationlocation.
 10. A method comprising: converting, at a call routing system,an audio input of a call to text; determining a first set of objects anda second set of actions from the text at the call routing system;determining, at the call routing system, that a particular action of thesecond set is included in a first portion of a synonym table; replacing,at the call routing system, the particular action in the second set witha synonym for the particular action from the synonym table to form amodified second set; pairing, at the call routing system, an object fromthe first set with an action from the modified second set to form anobject-action pair; and routing the call at the call routing systembased on the object-action pair.
 11. The method of claim 10, whereineach action of the modified second set is associated with acorresponding probability value that represents a probability that theaction corresponds to a voice command of a caller.
 12. The method ofclaim 11, wherein the action of the object-action pair is selected to bepaired with the object based on the probability value of the action. 13.The method of claim 10, wherein determining the first set of objects andthe second set of actions comprises parsing the text.
 14. The method ofclaim 10, wherein routing the call based on the object-action paircomprises: determining a routing location from a routing table based onthe object-action pair; and routing the call to the routing location.15. The method of claim 10, wherein the call is a voice over Internetprotocol call.
 16. The method of claim 10, further comprising receivingthe audio input via a web-based interface or a speech recognitioninterface of the call routing system.
 17. A computer-readable storagedevice comprising instructions executable by a processor to performoperations including: converting an audio input of a call to text;parsing the text to determine an action; determining that the action isincluded in a first portion of a synonym table; replacing the actionwith a synonym for the action from the synonym table to form a modifiedaction; pairing the modified action with an object from a set of objectsbased on the text to form an object-action pair; determining adestination from a routing table based on the object-action pair; androuting the call to the destination.
 18. The computer-readable storagedevice of claim 17, wherein the object is a particular synonym from thesynonym table that replaces a particular object parsed from the text.19. The computer-readable storage device of claim 17, wherein the actioncorresponds to a word string of the text.
 20. The computer-readablestorage device of claim 19, wherein the word string comprises a verb.